DSont: DSpace to ontology transformation

Farid, Humaira; Khan, Sharifullah; Javed, Muhammad Awais

doi:10.1177/0165551515591406

Cited by 8 publications

(9 citation statements)

References 22 publications

(49 reference statements)

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“…Data in ontologies is self‐describing because it is described through RDF tags, which makes it readable by machines and able to be used in reasoning to categorize and find associations in it (Butt and Khan ; Farid et al. ).…”

Section: Concepts and Definitionmentioning

confidence: 99%

“…Ontology models are structured with respect to concepts, properties, and associations (Horrocks ; Farid et al. ). In this framework, the concepts, properties, and associations of OntoEco are imported from external ontologies thanks to LOD [e.g., DBpedia (Cyganiak et al.…”

Section: Sswisi Ontology—ontoecomentioning

confidence: 99%

“…They represent a data source at a higher level of abstraction and manifest the semantics of a source. Data in ontologies is self-describing because it is described through RDF tags, which makes it readable by machines and able to be used in reasoning to categorize and find associations in it (Butt and Khan 2014;Farid et al 2016).…”

Section: Ontologiesmentioning

confidence: 99%

“…Ontology models are structured with respect to concepts, properties, and associations (Horrocks 2008;Farid et al 2016). In this framework, the concepts, properties, and associations of OntoEco are imported from external ontologies thanks to LOD [e.g., DBpedia (Cyganiak et al 2014), FOAF (Brickley and Miller 2010), schema.org (Cyganiak et al 2014), the European Pollutant Release and Transfer Register (http://prtr.ec.europa.eu/#/home), Toxic Release Inventory (www.epa.gov/toxics-release-inventory-tri-program), and WikiData (www.wikidata.org/wiki/Wikidata:Main_Page)].…”

Section: Sswisi Ontology-ontoecomentioning

confidence: 99%

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Social Semantic Web Framework for Industrial Synergies Initiation

Ghali

Frayret

2018

J of Industrial Ecology

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Summary Industrial synergies join two or more organizations that initially functioned as independent economic actors—that may originate from different sectors—together in order to share resources and exchange by‐products for mutual environmental, financial, and social benefits for its participants. Industrial symbioses (ISs) are networks of industrial synergies that can be initiated and created over time in various manners. In practice, the initiation of an industrial synergy, and particularly the identification of by‐product compatibilities, relies on direct or facilitated knowledge and information sharing, which is essential for discovering industrial synergy opportunities. Beyond its potential contribution to facilitate knowledge and information sharing among organizations, the Social Semantic Web (SSW) also has the potential to facilitate the initiation of industrial synergy by systematically and automatically identifying and recommending by‐products exchange compatibilities to potential partners. This framework exploits the ability of the sematic web to enable the search for analogies between potential partners within a region or district and existing industrial synergies around the world. This paper proposes the Social Semantic Web for Industrial Synergies Initiation (SSWISI) framework for the initiation of industrial synergies, which is based on the Social Semantic Web. The framework proposed in this paper adopts the concept of Linked Open Data (LOD), which enables the sharing and exchanging of information with external systems. This feature distinguishes the proposed framework from the existing approaches in its initiation of industrial synergies.

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Section: Concepts and Definitionmentioning

confidence: 99%

Section: Sswisi Ontology—ontoecomentioning

confidence: 99%

Section: Ontologiesmentioning

confidence: 99%

Section: Sswisi Ontology-ontoecomentioning

confidence: 99%

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Social Semantic Web Framework for Industrial Synergies Initiation

Ghali

Frayret

2018

J of Industrial Ecology

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“…The works from [9,10,11] provided a classification covering aspects such as the existence of an initial ontology, its application in reverse engineering, automation level, and others. Notably, the approaches [12,13,3,14,15,16,6,17,18,19,5,20] aim to convert relational data models to ontologies, by providing mapping rules that translate the elements of a database schema into concepts and relationships of an ontology. The W3C RDB2RDF Working Group provides as well a survey [21] of different approaches of mapping relational databases to ontologies, including the definition of a reference architecture to compare them.…”

Section: Introductionmentioning

confidence: 99%

Exploring logical and hierarchical information to map relational databases into ontologies

Tissot

Huve

Peres

et al. 2019

IJMSO

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Ontologies are formal specifications of conceptualizations. Their designs require to understand the concepts involved in the domain to be mapped. One well-known method to produce ontologies is to extract their concepts from relational databases. We conducted a practical study over a real-world scenario on applying existing rules and we identified open issues to be addressed, such as the utilization of logical metadata as a proper vocabulary, the implementation targeted to specific domains and mappings of hierarchical and self-hierarchical structures. In this paper, we present a novel approach that overcomes these issues. Our solution uses physical and logical models to enrich the terminology produced in the target ontology. It also contains a more comprehensive set of rules, taking into account instances and (self)hierarchies. We validate our approach with 2 experiments from the healthcare domain as input.

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